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检索条件"任意字段=6th IAPR International Conference on Pattern Recognition in Bioinformatics"

共 1869 条记录，以下是151-160 订阅

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High Utility pattern Mining: A Comprehensive Survey

High Utility Pattern Mining: A Comprehensive Survey

引用

international conference on Contemporary Computing and Informatics (IC3I)

作者： Chaitali Patil Sunita Dhotre Department of Computer Engineering Bharati Vidyapeeth (Deemed to be University) College Of Engineering Pune India

High-utility pattern mining, the process of searching highly relevant patterns in databases. pattern mining has gained popularity as a study area due to its many appliance in fields like bioinformatics, text mining, product recommendation, e-learning, and web click stream analysis. Effective decision- making requires high utility patterns, and demand for these patterns has grown recently. By classifying HUPM algorithms into a wide taxonomy, current comprehensive survey offers a overall, broad and strategic summary of advanced approaches in UPM. With a discussion of their benefits and disadvantages, a detailed analysis of current high-utility pattern mining techniques is offered. the study also covers current issues and practical difficulties in this field.

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Gene Expression pattern recognition Algorithm Based on Deep Learning

Gene Expression Pattern Recognition Algorithm Based on Deep ...

引用

IEEE international conference on Artificial Intelligence and Computer Applications (ICAICA)

作者： Xinzhuo Tu Yuanyuan Li Department of Pathology Air Force Medical Center PLA Beijing China China Medical University Shenyang China Nanchong Central Hospital Nanchong China

ISBN: (数字)9798350377255

ISBN: (纸本)9798350377262

With the rapid development of bioinformatics, the analysis of gene expression data has become crucial for understanding life processes, disease mechanisms, and drug development. For binary classification tasks, the performance of most traditional machine learning models is highly dependent on the quality of features, which often requires researchers to have a deep professional background, and artificially constructed feature vectors may lose the original information in protein sequences. this study aims to use deep learning models to extract high-order features from gene expression data, in order to solve the problem of gene expression pattern recognition. Firstly, by analyzing the limitations of traditional biological methods in gene recognition, a new approach combining recurrent neural networks (RNNs) and Transformer models is proposed. RNN can effectively capture temporal dependencies in sequence data, while Transformer, through its multi head self-attention mechanism, can more effectively process long sequence data and capture complex dependencies in gene expression sequences. through experiments on multiple publicly available gene expression datasets, it has been demonstrated that the Transformer model outperforms the RNN model, demonstrating its superiority in feature extraction. then, in response to the problem of low efficiency in processing long sequences with the Transformer model, this study further proposes the CNN Transformer model. the model first uses one-dimensional convolutional layers to extract local features from gene expression data, and then uses Transformer layers to capture long-range dependencies between these features. the experimental results show that the CNN Transformer model exhibits higher balanced accuracy on multiple independent test sets, demonstrating the effectiveness of the model structure. Finally, this article discusses the potential application value of this method in fields such as personalized medicine and early disease dia

关键词： Deep learning Proteins Recurrent neural networks Biological system modeling Transformers Feature extraction Data models Gene expression Convolutional neural networks bioinformatics

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CrackUDA: Incremental Unsupervised Domain Adaptation for Improved Crack Segmentation in Civil Structures 27th

CrackUDA: Incremental Unsupervised Domain Adaptation for Im...

引用

27th international conference on pattern recognition, ICPR 2024

作者： Srivastava, Kushagra Kancharla, Damodar Datta Tahereen, Rizvi Ramancharla, Pradeep Kumar Sarvadevabhatla, Ravi Kiran Kandath, Harikumar International Institute of Information Technology Hyderabad India

ISBN: (纸本)9783031781124

Crack segmentation plays a crucial role in ensuring the structural integrity and seismic safety of civil structures. However, existing crack segmentation algorithms encounter challenges in maintaining accuracy with domain shifts across datasets. To address this issue, we propose a novel deep network that employs incremental training with unsupervised domain adaptation (UDA) using adversarial learning, without a significant drop in accuracy in the source domain. Our approach leverages an encoder-decoder architecture, consisting of both domain-invariant and domain-specific parameters. the encoder learns shared crack features across all domains, ensuring robustness to domain variations. Simultaneously, the decoder’s domain-specific parameters capture domain-specific features unique to each domain. By combining these components, our model achieves improved crack segmentation performance. Furthermore, we introduce BuildCrack, a new crack dataset comparable to sub-datasets of the well-established CrackSeg9K dataset in terms of image count and crack percentage. We evaluate our proposed approach against state-of-the-art UDA methods using different sub-datasets of CrackSeg9K and our custom dataset. Our experimental results demonstrate a significant improvement in crack segmentation accuracy and generalization across target domains compared to other UDA methods - specifically, an improvement of 0.65 and 2.7 mIoU on source and target domains respectively. Additional details and code can be accessed from https://***. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Unsupervised learning

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Underwater Image Enhancement Based on Shallow Underwater Neural Network

Underwater Image Enhancement Based on Shallow Underwater Neu...

引用

pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Tianyi Lv Wenhui Yu Guangchao Rui Haijie Jia Jun Jiang Xiang Zhang Sixth Research Office Zheng Zhou Electromechanical Engineering Research Institute Zhengzhou China Sixth Research Office 713th Research Institute of China State Shipbuilding Corporation Limited Zhengzhou China

In recent years, due to the importance of underwater image enhancement in underwater robot, underwater vehicle and ocean engineering, more and more extensive research has been done. It has evolved from implementing physics-based solutions to using very cutting edge cnn and GANs. However, these cutting-edge algorithms often come at the cost of high computing power and time, which reduces the efficiency and portability of underwater working equipment using these algorithms. At the same time, these models have harsh requirements on data sets, leading to high cost of training and unfriendly to many underwater operations. therefore, this paper aims to propose a lightweight neural network structure, Shallow underwater neural network. these neural networks associate the original image directly with the output of each convolutional layer, preserving the original features while enhancing the image and avoiding gradient descent. the experimental results show that the model has a good effect on image enhancement, and the structure is lightweight.

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CsiTransformer: A Limited-sample 6G Channel State Information Feedback Model

CsiTransformer: A Limited-sample 6G Channel State Informatio...

引用

pattern recognition and Artificial Intelligence (PRAI), international conference on

作者： Qian Xiang Xiaodan Wang Xuan Wu Jie Lai Jiaxing He Yafei Song College of Air and Missile Defense Air Force Engineering University Xi’an China

In recent years, the channel state information (CSI) feedback model based on artificial intelligence has emerged as a significant research area for 6G pre-research. However, existing methods mainly rely on the data-driven capabilities of deep learning models, and insufficient attention has been given to the CSI feedback problem under limited-sample conditions. therefore, this paper proposed CsiTransformer, an improved Transformer-based CSI feedback model incorporating data augmentation techniques to handle the limited-sample CSI feedback problem. Experiments on different cellular scenarios demonstrate that CsiTransformer achieves an approximately 30% improvement in squared generalized cosine similarity compared to the traditional CsiNet when there are only 1000 samples for each scenario. Moreover, it shows a 49% improvement over the baseline model. the proposed CSI feedback model also exhibits good generalization across different cellular scenarios.

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Radar Small-Sample Working pattern recognition Based On Domain Adaptation

Radar Small-Sample Working Pattern Recognition Based On Doma...

引用

IEEE international conference on Electronic Information and Communication Technology (ICEICT)

作者： Fang Ye Wei Zhang Jingpeng Gao Sisi Jiang National Key Laboratory of Underwater Acoustic Technology College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Advanced Marine Communication and Information Technology College of Information and Communication Engineering Harbin Engineering University Harbin China College of Information and Communication Engineering Harbin Engineering University Harbin China

the number of radar working pattern samples is relatively small under non-cooperative environments, which will significantly degrade the performance of traditional deep learning recognition. this paper proposes an improved working pattern recognition algorithm based on domain adaptation to address the issue of minor sample conditions. We first present a barcode preprocessing method for converting 1D data to 2D data based on data mapping theory. then, the support vector machine classifier is improved by fusing Chebyshev metric distance and radial basis function. Finally, the classifier is trained using the extracted features, which are obtained by transferring different layers of the InceptionNeXt model. the simulation results show that the proposed algorithm is superior to the traditional algorithm and can effectively improve the recognition accuracy of small-sample working patterns.

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Research of Cluster Analysis Methods for Group Solutions of the pattern recognition Problem 6

Research of Cluster Analysis Methods for Group Solutions of ...

引用

6th international conference on Computer Science and Engineering, UBMK 2021

作者： Cherikbayeva, Lyailya Yerimbetova, Aigerim Daiyrbayeva, Elmira Al-Farabi Kazakh National University Department of Computer Science Almaty Kazakhstan Institute of Information and Computational Technologies CSMESRK Almaty Kazakhstan

ISBN: (纸本)9781665429085

this paper proposes the study of cluster analysis methods for solving the problem of pattern recognition, including group solution methods. the study selected methods for solving the problem of cluster analysis based on a group solution with incomplete training information, investigated and developed models of group solutions based on existing known algorithms. the novelty of the work consists in a combination of algorithms for collective cluster analysis and nuclear classification methods. Numerical experiments on test problems and a real hyperspectral image demonstrate the effectiveness of the proposed method, including in the presence of noisy data. © 2021 IEEE

关键词： Cluster analysis

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Enhancing Financial Trading Strategies with pattern recognition: A Systematic Literature Review of Methods, Trend and Challenge

Enhancing Financial Trading Strategies with Pattern Recognit...

引用

international conference on Cybernetics and Intelligent System (ICORIS)

作者： I Ketut Dedy Suryawan Kusrini Arief Setyanto Ema Utami Department of Informatics Doctorate Universitas Amikom Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

this study investigates how pattern recognition in financial markets can enhance trading strategies through a systematic literature review (SLR). the research focuses on the most frequently studied financial markets, commonly used pattern recognition methods, identified chart patterns, the integration of trading strategies with pattern recognition, and future challenges. this study is significant because many approaches to pattern recognition analysis are subjective and lack scientific evidence, leading to varied interpretations and uncertainties. the methodology includes formulating research questions, defining inclusion and exclusion criteria, conducting a comprehensive literature search, critically assessing included studies, data extraction and management, and result analysis and interpretation. this SLR covers 46 articles from databases such as Scopus, ACM Digital Library, IEEE Xplore, and Science Direct, published between 2015 and 2024, relevant to the topic. the findings show a dominant use of deep learning technologies, such as Convolutional Neural Networks (CNN), segmentation techniques, and fuzzy logic in recognizing patterns, particularly classic patterns like head and shoulders, triangles, double tops, and others, which contribute to enhancing trading strategies.

关键词： Deep learning Adaptation models Accuracy Uncertainty Magnetic heads pattern recognition Convolutional neural networks Tuning thermal stability Systematic literature review

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A Large Volume Natural Tamil Character Dataset 6th

A Large Volume Natural Tamil Character Dataset

引用

6th international conference on Computer Vision and Image Processing, CVIP 2021

作者： Arun, M. Arivazhagan, S. Ahila Priyadharshini, R. Centre for Image Processing and Pattern Recognition Mepco Schlenk Engineering College Tamil Nadu Sivakasi India

ISBN: (纸本)9783031113451

this paper focuses on recognizing the individual Tamil characters in images of natural scenes. Even though natural character dataset are available publicly for different languages, no specific standardized dataset is available for natural Tamil characters. In this work, we present a publicly available Tamil character dataset "Mepco-TamNatChar" for natural Tamil character recognition. this research work presents the preliminary results on natural Tamil character recognition using spatial and machine learnt features. We report an accuracy of 87.50 ± 1.42% for a train test ratio of 50:50 using machine learnt features. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Character recognition

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Deep Active Learning Image Classification Algorithm Based on Class-Wise Self-Knowledge Distillation 6

Deep Active Learning Image Classification Algorithm Based on...

引用

6th international conference on Artificial Intelligence and pattern recognition, AIPR 2023

作者： Pang, Yuliang Liu, Ying Hao, Yu Gong, Yanchao Li, Daxiang Xu, Zhijie Xi'an University of Posts and Telecommunications Center for Image and Information Processing China Xi'an University of Posts and Telecommunications Center for Image and Information Processing International Joint-Research Center for Wireless Communication and Information Processing China University of Huddersfield International Joint-Research Center for Wireless Communication and Information Processing United Kingdom

ISBN: (纸本)9798400707674

the success of image classification techniques based on deep learning relies heavily on a large amount of labeled data. However, the cost of data annotation is often expensive. this paper investigates active learning algorithms for image classification to reduce the cost of data annotation. However, traditional active learning algorithms for image classification suffer from overfitting issues due to training deep neural networks with millions of parameters using a small amount of labeled data. this makes it difficult for the model to effectively assess the information richness of unknown unlabeled samples and is detrimental to the selection of high-value samples. To alleviate these issues, this paper proposes a deep active learning-based image classification algorithm with class-wise self-knowledge distillation. this algorithm reduces overfitting and class-wise variations by matching or distilling the predictive distribution between different samples with the same label during training, thereby enabling a more accurate evaluation of the informativeness of unlabeled data by the active learning algorithm and improving the performance of the classification model. Additionally, an efficient Shuffle Attention mechanism is introduced to improve the sample selection strategy by combining spatial and channel feature information of the images. the proposed algorithm is compared with five active learning baselines on CIFAR10, CIFAR100, SVHN, and FashionMNIST datasets. Experimental results demonstrate the proposed algorithm exhibits superior classification performance. © 2023 ACM.

关键词： Image classification

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